Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images

Amitrano, Donato; Martino, Gerardo Di; Iodice, Antonio; Riccio, Daniele; Ruello, Giuseppe

doi:10.1109/tgrs.2018.2797536

Cited by 147 publications

(78 citation statements)

References 44 publications

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“…Even if the detection seems particularly accurate, the question of reproducibility of an object-oriented method on other geographical areas can be raised. These studies are undoubtedly interesting and a future comparison with other methods such as those presented in Amitrano [24] would be constructive.…”

Section: Discussionmentioning

confidence: 95%

“…Furthermore, the accuracy assessment using F-measure or F-Score cannot be compared with our results because it does not take into account true negatives. Li et al and Amitrano et al [23,24] studies offer a comparison with other different methods and globally obtained well detection of water. Although fully automated, the first method underline that the selection of reference image can be time consuming (several hours might be required).…”

Section: Discussionmentioning

confidence: 99%

“…Although fully automated, this method depends on the selection of a reference image and the authors underline that this step can be time consuming (several hours might be required). Amitrano et al [24] provided a unsupervised framework for Sentinel-1 flood mapping combining a textural analysis with a fuzzy classification on GRD amplitudes and a change detection approach, designed for end users and decision makers. Zhang et al [25] estimated the impact of hurricane Irma on Florida in terms of floods using both spectral analysis and InSAR from Sentinel-1 data to quantify the extent of the floods and water level variations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

et al. 2020

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In the future, climate change will induce even more severe hurricanes. Not only should these be better understood, but there is also a necessity to improve the assessment of their impacts. Flooding is one of the most common powerful impacts of these storms. Analyzing the impacts of floods is essential in order to delineate damaged areas and study the economic cost of hurricane-related floods. This paper presents an automated processing chain for Sentinel-1 synthetic aperture radar (SAR) data. This processing chain is based on the S1-Tiling algorithm and the normalized difference ratio (NDR). It is able to download and clip S1 images on Sentinel-2 tiles footprints, perform multi-temporal filtering, and threshold NDR images to produce a mask of flooded areas. Applied to two different study zones, subject to hurricanes and cyclones, this chain is reliable and simple to implement. With the rapid mapping product of EMS Copernicus (Emergency Management Service) as reference, the method confers up to 95% accuracy and a Kappa value of 0.75.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

et al. 2020

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“…There were differences between the optimal thresholds of the four SAR images, which may be linked to the noise (speckles) and resolution of the images. Despite all of the images being filtered to reduce the noise, finer resolution images contained more noise within the pixels than coarse images (a reduction in resolution declined speckles by multilook (3 × 3)) [45]. There was no noise observed when converting the HEC-RAS vector water to the raster format.…”

Section: Graphical Hst Optimizationmentioning

confidence: 99%

Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin

Quang

Tuấn

Hằng

et al. 2019

Water

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Synthetic Aperture Radar (SAR) remote sensing data can be used as an effective alternative to detect surface water and provide useful information regarding operational flood monitoring, in particular for the improvement of rapid flood assessments. However, this application frequently requires standard and simple, yet robust, algorithms. Although thresholding approaches meet these requirements, limitations such as data inequality over large spatial regions and challenges in estimating optimal threshold values remain. Here, we propose a new method for SAR water extraction named Hammock Swing Thresholding (HST). We applied this HST approach to four SAR remote sensing datasets, namely, Sentinel-1, ALOS-2, TerraSAR-X, and RadarSAT-2 for flood inundation mapping for a case study focusing on the Tam Nong district in the Vietnam Mekong delta. A 2D calibrated Hydrologic Engineering Centers River Analysis System (HEC-RAS) model was coupled with the HST outputs in order to estimate the optimal thresholds (OTs) where the SAR-based water masks fitted best with HEC-RAS’s inundation patterns. Our results showed that water levels extracted from Sentinel-1 data best agreed with the HEC-RAS water extent (88.3%), following by ALOS-2 (85.9%), TerraSAR-X (77.2%). and RadarSAT-2 (72%) at OTs of −15, 68, 21, and 35 decibel (dB), respectively. Generated flood maps indicated changes in the flood extent of the flooding seasons from 2010 and 2014–2016 with variations in spatial extent appearing greater in the TerraSAR-X and RadarSAT-2 higher resolution maps. We recommend the use of OTs in applications of flood monitoring using SAR remote sensing data, such as for an open data cube (ODC).

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“…Examples in the literature include the use of satellite data for mapping the extent of a burned area using optical and thermal sensors of different spatial resolution [21][22][23][24] and many methods are particularly developed for Sentinel data (e.g., [25,26]). Various approaches were developed for example for the automatic flood detection [27,28], and the free and open access policy of Copernicus Sentinel-1 triggered the development of more [29][30][31]. An important asset for monitoring disasters of the Sentinel-1 constellation is the frequency of data acquisition (six days) and the fact that they can be downloaded within an hour and 24 hours of reception depending on the severity of the event.…”

Section: Introductionmentioning

confidence: 99%

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

et al. 2020

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The successful implementation of the European Commission's Common Agricultural Policy (CAP) and the insurance coverage in case of a natural disaster requires precise and regular mapping of crop types and detailed delineation of the disasters' effects by frequent and accurate controls. Free and open access policy to Copernicus Sentinel data offers a big volume of data to the users on a consistent and complete basis. Today, the Sentinels are involved in an increasing number of agriculture applications, but their effective exploitation is still being investigated and the development of efficient tools, aligned to the user's needs, is yet to be realised. To this end, the DiAS (Disaster and Agriculture Sentinel Applications) project proposes methods for decision support in agriculture using Sentinel data for crop type mapping, as well as mapping of the extend of fire and flood effects in agricultural areas. The DiAS Decision Support System (DSS) is designed in consultation with potential users in participatory approach and aims to provide a prototype tool, which provides assistance to the responsible paying agencies and insurance organizations to make decisions on farmers' subsidies and compensations. The DiAS DSS prototype and its functionalities are presented in this paper and its use is demonstrated through example applications for two test sites in Greece. The DiAS DSS demonstrates the necessity for the development of similar tools, as this emerges from the user's requirements, and wishes to stimulate and inspire further research and development.Sustainability 2020, 12, 1233 2 of 20 filed inspections may determine the eligible areas for subsidy or compensation precisely, however they require a lot of effort and time and some factors like the adverse weather conditions, or remote areas, may cause large delays in the payments, which has an impact on the agricultural processes and development of the country. [1,2]. Lately, the control efforts involve the examination of satellite imagery, but this refers to very high-resolution commercial imagery and visual image interpretation or rare case image classification of single-date images [3]. These procedures improve the control efforts in some cases, but they have a high cost and their effectiveness relies mostly on the skills of the photo-interpreter and therefore cannot always offer timely, reliable, and robust results [4].There are several attempts for automating the mapping procedures in agriculture-related applications. For example, in terms of crop types mapping, the EU is coordinating the Monitoring Agricultural Resources (MARS) initiative [5] with the help of the Joint Research Center (JRC) in order to facilitate the coordination of the CAP. In the framework of MARS and following guidelines from the JRC, each EU member state is building a geographic information Land Parcel Identification System (LPIS) by interpreting very-high-resolution satellite images. Along with LPIS, the JRC is in charge of the image acquisition and it provides scientific and technical suppor...

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Unsupervised Rapid Flood Mapping Using Sentinel-1 GRD SAR Images

Cited by 147 publications

References 44 publications

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

A Sentinel-1 Based Processing Chain for Detection of Cyclonic Flood Impacts

Hydrological/Hydraulic Modeling-Based Thresholding of Multi SAR Remote Sensing Data for Flood Monitoring in Regions of the Vietnamese Lower Mekong River Basin

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

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